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1.
Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach
Vid Ravnik, Marko Jukič, Urban Bren, 2023, original scientific article

Published in DKUM: 06.05.2024; Views: 69; Downloads: 1
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Design of Tetra-Peptide Ligands of Antibody Fc Regions Using In Silico Combinatorial Library Screening
Marko Jukič, Sebastjan Kralj, Anja Kolarič, Urban Bren, 2023, original scientific article

Abstract: Abstract Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries’ display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.
Keywords: peptide design, in silico combinatorial library, peptide combinatorial library, peptide library design, high-throughput virtual screening, peptide molecular docking, antibody purification, peptide drug design, recombinant peptide libraries
Published in DKUM: 01.12.2023; Views: 251; Downloads: 73
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6.
Naive prediction of protein backbone phi and psi dihedral angles using deep learning
Matic Broz, Marko Jukič, Urban Bren, 2023, original scientific article

Abstract: Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.
Keywords: protein structure prediction, backbone dihedral angles, deep neural network, fully connected neural network, FCNN, protein secondary structure prediction
Published in DKUM: 01.12.2023; Views: 290; Downloads: 157
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7.
Analiza gruč kemijskega prostora protibakterijskih učinkovin : diplomsko delo visokošolskega strokovnega študijskega programa I. stopnje
Aljaž Knez, 2023, undergraduate thesis

Abstract: Vsi smo se že vsi vsaj enkrat soočili z bakterijsko okužbo, katero zdravljenje je potekalo z antibiotiki, ki smo ga v diplomskem delu uporabljali kot sinonim za protibakterijske učinkovine. Protibakterijske učinkovine se od ostalih zdravil razlikujejo predvsem po drugačnih fizikalno kemijskih lastnostih, delovanju, cilju delovanja, spektru delovanja in uporabi. Zlati dobi razvoja antibiotikov je zaradi vse hitrejše odpornosti bakterij sledila temačna doba, v kateri farmacevtska podjetja zaradi negotovosti in dragega razvoja niso želela vlagati v razvoj antibiotikov. Raje so se posvečala zdravilom, ki so jim predstavljala večjo gotovost in boljši zaslužek. Tako je bakterijska rezistenca postala ena največjih globalnih težav javnega zdravstva v 21. stoletju. Bolj kot na sam razvoj novih antibiotikov smo se osredotočili na predstavitev mehanizmov bakterijske rezistence in predvsem iskali rešitve kako se soočiti z bakterijam. Preučili smo kemijski prostor glede na izračunani set kemijskih deskriptorjev. Nato pa izvedli kemoinformacijsko analizo gruč in ugotovili, da poleg izračunanega za uspešno kvalifikacijo potrebujemo tudi strukturne informacije. Pri analizi kemijskega prostora smo opazili, da sta kemijska prostora protibakterijskih učinkovin in ostalih zdravil različna. Rezultati bodo koristili pri nadaljnji kemijski analizi in razvoju novih antibiotikov. Edinstven pristop pri razvoju zdravil v zgodnjih fazah predstavlja kemoinfomatika, ki bo v prihodnosti predstavljala ključen dejavnik, ki bo prihranil čas in denar.
Keywords: protibakterijske učinkovine, antibiotiki, bakterijska rezistenca, kemijski prostor, kemoinformatika, razvrščanje antibiotikov v gruče
Published in DKUM: 05.10.2023; Views: 282; Downloads: 49
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Načrtovanje pentapeptidov za vezavo na fc regijo protiteles : diplomsko delo visokošolskega strokovnega študijskega programa I. stopnje
Benjamin Stradar, 2023, undergraduate thesis

Abstract: Razvoj novih peptidnih zdravilnih učinkovin je zelo zahtevna naloga kemijske in farmacevtske industrije. Delo lahko olajša uporaba računalniških pristopov in simulacij. Z njimi lahko uspešno modeliramo interakcije, ki so ključne pri razumevanju delovanja in razvoja novih zdravilnih učinkovin. Peptidi zajemajo prednosti majhnih molekul ter tarčno specifičnost večjih struktur, kot so proteini. V diplomski nalogi smo za načrtovanje novih potencialnih struktur peptidov uporabili računalniške in bioinformacijske pristope kot sta mulekulsko sidranje in kemoinformacijska analiza. Za načrtovanje peptidov smo se osredotočili predvsem na tarčo Fc regije protiteles, kjer smo ustrezno strukturo pridobili na prosto dostopnem spletnem mestu https://www.rcsb.org/ (PDB ID: 5U52). Načrtovali in identificirali smo potencialne pentapeptide z uporabo struktur iz predhodno načrtovane knjižnice tetrapeptidov. Za pripravo knjižice struktur pentapeptidov in analizo rezultatov pa smo uporabili 100 najboljših predhodno identificiranih tetrapeptidov. Nato smo izvedli molekulsko sidranje in uporabili programsko opremo CmDock. Podatke smo nato analizirali z uporabo programov PyMOL in PLIP. Uporabljen protokol je v primerjavi z uporabo kombinatoričnih knjižnic omogočil učinkovitejše
Keywords: pentapeptidi, Fc regija, molekulsko sidranje, PyMol, CmDock, peptidno sidranje
Published in DKUM: 25.09.2023; Views: 349; Downloads: 27
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9.
Neuropilin (NRPs) related pathological conditions and their modulators
Matic Broz, Anja Kolarič, Marko Jukič, Urban Bren, 2022, review article

Abstract: Neuropilin 1 (NRP1) represents one of the two homologous neuropilins (NRP, splice variants of neuropilin 2 are the other) found in all vertebrates. It forms a transmembrane glycoprotein distributed in many human body tissues as a (co)receptor for a variety of different ligands. In addition to its physiological role, it is also associated with various pathological conditions. Recently, NRP1 has been discovered as a coreceptor for the SARS-CoV-2 viral entry, along with ACE2, and has thus become one of the COVID-19 research foci. However, in addition to COVID-19, the current review also summarises its other pathological roles and its involvement in clinical diseases like cancer and neuropathic pain. We also discuss the diversity of native NRP ligands and perform a joint analysis. Last but not least, we review the therapeutic roles of NRP1 and introduce a series of NRP1 modulators, which are typical peptidomimetics or other small molecule antagonists, to provide the medicinal chemistry community with a state-of-the-art overview of neuropilin modulator design and NRP1 druggability assessment.
Keywords: neuropilins, computer-aided drug design, in silico drug design, receptor modulator design, peptidomimetics, small-molecule antagonists, cancer, COVID-19, neuropathic pain
Published in DKUM: 22.08.2023; Views: 259; Downloads: 25
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10.
The effect of the Ala16Val mutation on the secondary structure of the manganese superoxide dismutase mitochondrial targeting sequence
Matic Broz, Veronika Furlan, Samo Lešnik, Marko Jukič, Urban Bren, 2022, original scientific article

Abstract: Manganese Superoxide Dismutase (MnSOD) represents a mitochondrial protein that scavenges reactive oxygen species (ROS) responsible for oxidative stress. A known single nucleotide polymorphism (SNP) rs4880 on the SOD2 gene, causing a mutation from alanine to valine (Ala16Val) in the primary structure of immature MnSOD, has been associated with several types of cancer and other autoimmune diseases. However, no conclusive correlation has been established yet. This study aims to determine the effect of the alanine to valine mutation on the secondary structure of the MnSOD mitochondrial targeting sequence (MTS). A model for each variant of the MTS was prepared and extensively simulated with molecular dynamics simulations using the CHARMM36m force field. The results indicate that the alanine variant of the MTS preserves a uniform α-helical secondary structure favorable for the protein transport into mitochondria, whereas the valine variant quickly breaks down its α-helix. Thus, the alanine MTS represents the more active MnSOD variant, the benefits of which have yet to be determined experimentally.
Keywords: manganese superoxide dismutase, polymorphism rs4880, mutation Ala16Val, molecular dynamics simulations, oxidative stress
Published in DKUM: 21.08.2023; Views: 287; Downloads: 10
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